Vinyals et al.[1]} stimulated much work in this area by introducing SC2LE, a SC2 environment for reinforcement learning, They note the relative ease of creating RL agents from scratch to learn the mini games, which test micromanagement skills, whereas they advocate the use of replay datasets as a better way to achieve proficiency on the full game. Tang et al. used this environment to demonstrate an RL agent that could do macromangement using Convolutional Neural Network Fitted Q-Learning (CNNFQ) [2]}.
